As a basis of understanding the economical dispatch arrangement for a boiler system having a cogenerative capability, one must first understand the boiler system itself and the parameters under which it is operating. To aid in this understanding, reference is hereby made to U.S. Pat. No. 4,604,714 issued to R. E. Putman Jr. et al on Aug. 5, 1986 which is incorporated herein by reference. From this system, it is known that the ultimate goal is to obtain the targeted amount of steam from a two boiler configuration each boiler of which can be fired from one of two fuel sources, and to do so at the least total cost in terms of purchased fuel. According to this configuration, it is required that the economical dispatching arrangement monitor and act on a number of primary parameters such as: the relationship between the efficiency and the load for each of the fuels on each of the boilers; the present steam flows from and fuel flows to each of the boiler units; and the automatic/manual status of the fuel and boiler master load control loops. In addition, other data to be considered includes the prices for each of the fuels and the heating values associated with each.
One approach to handling a number of variables for a linear or a non-linear system such as the present system falls under, has been by use of the SSDEVOP system originally developed by G.E.P. Box for optimizing chemical process performance. This approach was presented in an article entitled: "Evolutionary Operation: A Method for Increasing Industrial Productivity," Applied Statistics, Vol. VI, No. 2, pp. 3-23.
According to this method, a set of variables which are typically three in number, are initially assigned a base set of feasible or actual values. The experimental design consists of a set of four tests which each contain a different combination of these variables perturbed about the base set. A perturbation value (delta) is assigned to each variable according to a predetermined pattern and the cost associated with each test is then calculated. The worst case values; that is, the ones having the highest cost associated therewith, are subtracted from twice the means of the three best case values with the final set of values becoming the new base set of variables from which the next calculation is taken. This process is repeated until no further improvement in the calculated response is detected.
Though this method has proven effective for certain applications, particularly where the number of perturbable variables is low, there are certain deficiencies with this method when it is applied to a process having a larger number of perturbable variables that must be considered before an accurate response can be achieved. For instance, with a larger number of perturbable variables, there is a significantly longer time required for the process to converge on the optimum values for the variables. Additionally, the way this method has been used in the past has been to manually disturb the process to generate the desired test cases, a step which has the effect of unnecessarily disturbing the process. It has also been found that, because of the time deficiency when using a larger number of variables and the need to manually disturb the process, this method suffers in an application which does not involve a steady state process. This is true since, in a non-steady state process, the optimum process operating point can vary over a greater range than that of a steady state process and this method is only accurate where this point does not vary in any appreciable manner, as can be appreciated by the fact that this method is attempting to converge on a particular set of assignments for the variables. It has also been found with this method that when the variables are close to or at the operating constraints of the process, the accuracy is sacrificed.